A presentation I have presented as a part of the Saudi Board of Community Medicine, Western Region. It simplifies the ideas behind hypothesis and hypothesis testing, also contains many different approaches of choosing the best statistical tests needed in any study.

1. 1
Biostatistics
Simplified
PREPARED & PRESENTED BY:

DR. M. ALHEFZI

DR. N. ALOTAIBI

DR. A. KHALAWI

DR. B. ALHEJAILI

DR. M. ALGOTHAMI

DR. S. ALGHAMDI
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A d v a n c e d

2. 2

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
Summarization

Analysis – inference.

WHY
BIOSTAT ?!
Collection
Interpretation of the
results
Abhaya Indrayan (2012). Medical Biostatistics. CRC Press. ISBN 978-1-4398-8414-0. (QR-code above).

3. 3
Philosophy behind Hypothesis
What is a hypothesis?
CHANCE?!
Mill’s Cannons / Methods – Agreement, Difference, Concomitant, Residues
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4. 4
Am I right or wrong ?!
Is it the truth ?!
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5. 5
•
BIAS?
CONFOUNDING?
CHANCE?
CAUSE / EFFECT?
•
GENERALIZABILITY!
•
•
SIGNIFICANCE
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•

6. 6
My Hypothesis
Ha
TEST!
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7. 7
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8. 8
In other words …
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9. 9


So, what
language
do we
speak in
biostat?
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MATH?
MEAN, MEDIAN, MODE, RANGE
…

AREA UNDER THE
CURVE, VARIANCE, SD …

MEDICINE?

EXPOSURE, DISEASE, OUTCOME,
EFFECTIVITY, PREVENTION

RELATIVE RISK, ABSOLUTE RISK

10. 10


MEDIAN.

Biostatisticians’
language
MEAN (μ).
MODE.

AREA UNDER THE
CURVE:


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Variance.
SD (σ).

11. 11
Biostatisticians’ language
Standard Deviation (SD)
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12. 12
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Photo courtesy of Judy Davidson, DNP, RN

13. “
13
WE MAKE MISTAKES!
”
IN ORDER TO AVOID THEM, WE NEED TO SET RANGES FOR CHANCE, ALSO SET OUR CRITICAL LIMITS. TO END UP WITH A
MASTERPIECE OF EVIDENCE!


p-value

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H0
CI *
vs. α level

14. 14
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15. 15
Test
Hypothesis
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16. 16

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
STEPS.

Test
Hypothesis
ASSUMPTIONS.
TESTS.

17. 

17
LARGE SAMPLE
SIZE.
NORMAL
DISTRIBUTION.

Gaussian Dist.

NO
MULTICOLINIARITY.
KNOWN
& σ ).

ASSUMPTIONS
HOMOGENEITY.

Test Hypothesis

INDEPENDENCY.
– Differs for each test.
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(μ

18. 1)
RQ ?
2)
H0 & H 1
3)
TEST &
ASSUMPTIONS.
Test Hypothesis
4)
α LEVEL, P-VALUE.
– 7 steps of hypothesis testing.
5)
TEST STATISTIC (DF).
6)
DECISION.
7)
CONCLUSION
(YES/NO).
STEPS
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19. 19
Test Hypothesis
TEST
STATISTICS
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20. 20
Each member
in this group is
exclusively
linked to it
Dependency Concept
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Output
changes
whenever
input do so

21. Data Analysis
•
•
•
•
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Randomization.
Restriction.
Matching.
Stratification.
21

22. 22
Statistical Tests
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23. 23
Statistical Tests
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24. 24
Choosing a Bivariate test
Dependent VA (outcome, output)
Indep. VA
Input
exposure
2 Cat.
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>2 Cat.
Continuous
Cat.
χ2
χ2
t-test
> 2 Cat.
χ2
χ2
ANOVA
Continuous
t-test
ANOVA
Correlation
Linear Regression

25. 25
Continuous Data
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26. 26
Ordinal Data
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27. 27
Categorical Data
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28. Choosing the Best Statistical Test
28
Comparison the difference between
groups
Cat. VA (2)  Cont. VA
Independent sample
(t-test)
Mann-Whitney
(U test)
Cont. Dep. VA  same group
Paired Sample
(t-test)
Wilcoxon
Cat. VA (>3)  Cont. VA
One Way
ANOVA
Kruskal Wallis
Cat. VA  Cat. VA
Chi-Square
(χ2 )
McNemar
Association / Strength of
Relationship
Cont. VA  Cont. VA
Pearson (r)
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Spearman’s ρ
Prediction
Cont. VA  Cont. or
Cat.
MLR
SLR (Bivariate)
PMT
Cont. VA  Cont. +
Other VAs
NPMT
Cat. VA  >1 Other
VAs
Logistic
Regression
By @alhefzi

29. 29
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30. 30
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31. 31
Considerations

Normal Distribution & Sample Size.

Large sample size ().


Otherwise, do (Kolmogorov Smirnov) to check normality.


Shape by inspection.
If NPMT with Large sample size ()  less powerful than a PMT.
Gaussian Distribution ().


PMT with Non-Gaussian distribution ()  CLT.

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NPMT with Gaussian distribution, “small” sample size ().  (small, Non-Gaussian)  (
 p-value).
PMT with Non-Gaussian distribution, “small” sample size ()  CLT won’t
work, inaccurate p-value.

32. 32
Considerations

1 or 2 sided p-value

H0 ().


Question: WHICH p-value is larger and why? (1 or 2 sided)?


Based on: equal population means. Otherwise, any discrepancy is due to chance!!
i.e. when formulating your Ha; consider “larger” critical p-value accordingly!
Go for 1 sided (if)



You have formulate a “directional” hypothesis.
Set it BEFORE data collection. Otherwise, you will have to attribute the difference to chance.
Go for 2 sided (if)


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Unsure or in doubt of your hypothesis direction.
Set it BEFORE data collection. Otherwise, you will have to attribute the difference to chance.

33.  The critical value is the
number that separates the
“blue zone” from the
middle (± 1.96 this
example).
 In a t-test, in order to be
statistically significant the t
score needs to be in the
“dark-blue zone”.
 If α = .05, then 2.5% of the
area is in each tail
2-tailed test
Biostatisticians’ language
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33

34.  The critical value is either +
or -, but not both.
 e.g. in a t-test
 In this case, you would
have statistical significance
(p < .05) if t ≥ 1.645.
1-tailed test
Biostatisticians’ language
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34

35. 35
 Any number squared is a
positive number.
 Therefore, area under the
curve starts at 0 and goes
to infinity (∞).
 To be statistically
significant, needs to be in
the upper 5% (α = .05).
 Compares observed
frequency to what we
expected.
Chi-Square (χ2) – as an example
Biostatisticians’ language
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Published on STAT 100 - Statistical Concepts and Reasoning (QR-code above)

36. 36
Considerations

Regression or Correlation
Correlation
Regression
X&Y are important to be
set




Swapping X&Y in the
curve gives different
results


In Gaussian distribution
Pearson
SLR, MLR
NPMT
Spearman’s rho
Logistic
Regression
Cause-effect relationship
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37. @alhefzi
End of Part I
Thank you…
QUESTIONS?
37